Identification of product life cycle models by autoregression–moving average models and Groebner’s bases
AbstractThe authors offer the analytical models of product life cycle and the approach towards their classification based on the models of autoregression–moving average and using the Groebner bases for solving the normal systems of non-linear polynomial equations, received after using the least-squares method. The characteristics of modeling and forecasting fidelity have been also elaborated, concerning the sales data for cars, data for oil production, as well as interest of Google users towards cell phone models and guide-books edition.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Publishing House "SINERGIA PRESS" in its journal Applied Econometrics.
Volume (Year): 25 (2012)
Issue (Month): 1 ()
Contact details of provider:
Web page: http://appliedeconometrics.cemi.rssi.ru/
product life cycle models; ARMA; OLS method; Groebner bases; car; cell phone; guidebook;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- D91 - Microeconomics - - Intertemporal Choice - - - Intertemporal Household Choice; Life Cycle Models and Saving
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 20(1), pages 134-44, January.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anatoly Peresetsky).
If references are entirely missing, you can add them using this form.